Pain Mechanisms Associated With Disease Activity in Patients With Rheumatoid Arthritis Treated With Disease-Modifying Antirheumatic Drugs: A Regression Tree Analysis
Bibliographic record
Abstract
OBJECTIVE: Although pain affects the assessment of disease activity in patients with rheumatoid arthritis (RA), pain is not always directly related to peripheral joint inflammation. Peripheral and central nervous system regulatory mechanisms also affect pain perception. We used regression tree methodology to identify mechanisms most predictive of disease activity after disease-modifying antirheumatic drug (DMARD) treatment. METHODS: Disease activity was evaluated using the Disease Activity Score in 28 joints (DAS28) in 176 patients with RA, before and after starting a DMARD. Quantitative sensory testing (QST), including pressure pain thresholds (PPTs), temporal summation, and conditioned pain modulation (CPM), were used to assess pain mechanisms. Regression tree methodology was used to determine the QST modalities most predictive of DAS28 after DMARD treatment. RESULTS: This analysis identified 4 groups defined by baseline DAS28 category and either knee PPT (a combined measure of peripheral and central nervous system dysregulation) or CPM (a measure of descending pain inhibition). Among patients starting with low/moderate disease activity, lower knee PPT (PPT ≤ 4.65 kgf) most strongly predicted higher posttreatment disease activity (group 1 mean DAS28 2.8 [SD 1.0] vs group 2 mean DAS28 3.5 [SD 1.0]). Among patients starting with high baseline disease activity, less efficient descending pain modulation (CPM ≤ 1.55) most strongly predicted higher posttreatment disease activity (group 3 mean DAS28 3.4 [SD 1.4] vs group 4 mean DAS28 4.6 [SD 1.1]). CONCLUSION: These results highlight the importance of identifying and treating aberrant peripheral and central pain regulation in patients with RA starting or switching DMARD therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".